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105213782/cell_31 | [
"text_html_output_1.png"
] | X_train | code |
105213782/cell_46 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
knn_classifier = KNeighborsClassifier(n_neighbors=3, metric='minkowski', p=3)
knn_classifier.fit(X_train, Y_train) | code |
105213782/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt #plotting
import numpy as np
import pandas as pd
import seaborn as sns #visualization
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data.drop(['id'], axis=1, inplace=True)
data.isnull().sum()
data.isnull().sum()
strokes = len(data[data['stroke'] == 1])
no_strokes = data[data.stroke == 0].index
random_indices = np.random.choice(no_strokes, strokes, replace=False)
stroke_indices = data[data.stroke == 1].index
under_sample_indices = np.concatenate([stroke_indices, random_indices])
udata = data.loc[under_sample_indices]
sns.countplot(data=udata, x='stroke')
plt.show() | code |
105213782/cell_14 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
import pandas as pd
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data.drop(['id'], axis=1, inplace=True)
data.isnull().sum()
data.isnull().sum()
from sklearn import preprocessing
label_encoder = preprocessing.LabelEncoder()
data['gender'] = label_encoder.fit_transform(data['gender'])
data['gender'].unique()
data['ever_married'] = label_encoder.fit_transform(data['ever_married'])
data['ever_married'].unique() | code |
105213782/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data.drop(['id'], axis=1, inplace=True)
data.isnull().sum()
data.isnull().sum()
data.info() | code |
105213782/cell_70 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import precision_score, recall_score, f1_score,accuracy_score #metrics
from sklearn.metrics import roc_auc_score, roc_curve #metrics
from sklearn.metrics import roc_curve, roc_auc_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
import numpy as np
import pandas as pd
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data.drop(['id'], axis=1, inplace=True)
data.isnull().sum()
data.isnull().sum()
strokes = len(data[data['stroke'] == 1])
no_strokes = data[data.stroke == 0].index
random_indices = np.random.choice(no_strokes, strokes, replace=False)
stroke_indices = data[data.stroke == 1].index
under_sample_indices = np.concatenate([stroke_indices, random_indices])
udata = data.loc[under_sample_indices]
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(X_train, Y_train)
Y_pred = lr.predict(X_test)
features = np.array([[0, 78, 0, 0, 1, 3, 0, 60, 28.8, 1]])
prediction = lr.predict(features)
from sklearn.neighbors import KNeighborsClassifier
knn_classifier = KNeighborsClassifier(n_neighbors=3, metric='minkowski', p=3)
knn_classifier.fit(X_train, Y_train)
Y_pred_knn = knn_classifier.predict(X_test)
features = np.array([[0, 78, 0, 0, 1, 3, 0, 60, 28.8, 1]])
prediction = knn_classifier.predict(features)
from sklearn import tree
dt_classifier = DecisionTreeClassifier()
dt_classifier.fit(X_train, Y_train)
Y_pred_dtc = dt_classifier.predict(X_test)
features = np.array([[0, 78, 0, 0, 1, 3, 0, 60, 28.8, 1]])
prediction = dt_classifier.predict(features)
from sklearn.naive_bayes import GaussianNB
gnb_classifier = GaussianNB()
gnb_classifier.fit(X_train, Y_train)
Y_pred_gnb = gnb_classifier.predict(X_test)
features = np.array([[0, 78, 0, 0, 1, 3, 0, 60, 28.8, 1]])
prediction = gnb_classifier.predict(features)
print('Accuracy:', accuracy_score(Y_test, Y_pred_gnb))
print('Precision', precision_score(Y_test, Y_pred_gnb))
print('Recall', recall_score(Y_test, Y_pred_gnb))
print('F1 score', f1_score(Y_test, Y_pred_gnb))
print('ROC score', roc_auc_score(Y_test, Y_pred_gnb)) | code |
105213782/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data | code |
105213782/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(X_train, Y_train) | code |
18117432/cell_21 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.isnull().sum()
train_df['Initial'] = 0
for i in train_df:
train_df['Initial'] = train_df.Name.str.extract('([A-Za-z]+)\\.')
pd.crosstab(train_df.Initial, train_df.Sex).T.style.background_gradient(cmap='gist_rainbow')
train_df['Initial'].replace(['Dr', 'Mlle', 'Mme', 'Ms', 'Major', 'Lady', 'Countess', 'Jonkheer', 'Col', 'Rev', 'Capt', 'Sir', 'Don'], ['Other', 'Miss', 'Miss', 'Miss', 'Mr', 'Mrs', 'Mrs', 'Other', 'Other', 'Other', 'Mr', 'Mr', 'Mr'], inplace=True)
pd.crosstab(train_df.Initial, train_df.Sex).T.style.background_gradient(cmap='gist_rainbow')
train_df.groupby('Initial')['Age'].mean()
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Mr'), 'Age'] = 32.5
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Mrs'), 'Age'] = 36
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Master'), 'Age'] = 4.5
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Miss'), 'Age'] = 22
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Other'), 'Age'] = 44.5
sns.stripplot(x='Initial', y='Age', data=train_df, jitter=True, hue='Survived') | code |
18117432/cell_25 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.isnull().sum()
train_df['Initial'] = 0
for i in train_df:
train_df['Initial'] = train_df.Name.str.extract('([A-Za-z]+)\\.')
pd.crosstab(train_df.Initial, train_df.Sex).T.style.background_gradient(cmap='gist_rainbow')
train_df['Initial'].replace(['Dr', 'Mlle', 'Mme', 'Ms', 'Major', 'Lady', 'Countess', 'Jonkheer', 'Col', 'Rev', 'Capt', 'Sir', 'Don'], ['Other', 'Miss', 'Miss', 'Miss', 'Mr', 'Mrs', 'Mrs', 'Other', 'Other', 'Other', 'Mr', 'Mr', 'Mr'], inplace=True)
pd.crosstab(train_df.Initial, train_df.Sex).T.style.background_gradient(cmap='gist_rainbow')
train_df.groupby('Initial')['Age'].mean()
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Mr'), 'Age'] = 32.5
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Mrs'), 'Age'] = 36
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Master'), 'Age'] = 4.5
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Miss'), 'Age'] = 22
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Other'), 'Age'] = 44.5
train_df['FamilySize'] = train_df['Parch'] + train_df['SibSp']
sns.barplot(x='FamilySize', y='Survived', data=train_df) | code |
18117432/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.isnull().sum()
train_df['Initial'] = 0
for i in train_df:
train_df['Initial'] = train_df.Name.str.extract('([A-Za-z]+)\\.')
pd.crosstab(train_df.Initial, train_df.Sex).T.style.background_gradient(cmap='gist_rainbow')
train_df['Initial'].replace(['Dr', 'Mlle', 'Mme', 'Ms', 'Major', 'Lady', 'Countess', 'Jonkheer', 'Col', 'Rev', 'Capt', 'Sir', 'Don'], ['Other', 'Miss', 'Miss', 'Miss', 'Mr', 'Mrs', 'Mrs', 'Other', 'Other', 'Other', 'Mr', 'Mr', 'Mr'], inplace=True)
pd.crosstab(train_df.Initial, train_df.Sex).T.style.background_gradient(cmap='gist_rainbow')
train_df.groupby('Initial')['Age'].mean()
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Mr'), 'Age'] = 32.5
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Mrs'), 'Age'] = 36
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Master'), 'Age'] = 4.5
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Miss'), 'Age'] = 22
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Other'), 'Age'] = 44.5
f,ax=plt.subplots()
train_df['Survived'].value_counts().plot.pie(explode=[0,0.05],autopct='%1.1f%%',shadow=True)
ax.set_title('Survived')
ax.set_ylabel('')
plt.show()
f, ax = plt.subplots(1, 1, figsize=(6, 5))
train_df['Embarked'].value_counts().plot.pie(explode=[0, 0, 0], autopct='%1.1f%%', ax=ax)
plt.show() | code |
18117432/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.isnull().sum()
train_df['Initial'] = 0
for i in train_df:
train_df['Initial'] = train_df.Name.str.extract('([A-Za-z]+)\\.')
pd.crosstab(train_df.Initial, train_df.Sex).T.style.background_gradient(cmap='gist_rainbow')
train_df['Initial'].replace(['Dr', 'Mlle', 'Mme', 'Ms', 'Major', 'Lady', 'Countess', 'Jonkheer', 'Col', 'Rev', 'Capt', 'Sir', 'Don'], ['Other', 'Miss', 'Miss', 'Miss', 'Mr', 'Mrs', 'Mrs', 'Other', 'Other', 'Other', 'Mr', 'Mr', 'Mr'], inplace=True)
pd.crosstab(train_df.Initial, train_df.Sex).T.style.background_gradient(cmap='gist_rainbow')
train_df.groupby('Initial')['Age'].mean()
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Mr'), 'Age'] = 32.5
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Mrs'), 'Age'] = 36
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Master'), 'Age'] = 4.5
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Miss'), 'Age'] = 22
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Other'), 'Age'] = 44.5
sns.violinplot(x='Sex', y='Age', data=train_df, hue='Survived', split=True) | code |
18117432/cell_6 | [
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
train_df.describe() | code |
18117432/cell_26 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.isnull().sum()
train_df['Initial'] = 0
for i in train_df:
train_df['Initial'] = train_df.Name.str.extract('([A-Za-z]+)\\.')
pd.crosstab(train_df.Initial, train_df.Sex).T.style.background_gradient(cmap='gist_rainbow')
train_df['Initial'].replace(['Dr', 'Mlle', 'Mme', 'Ms', 'Major', 'Lady', 'Countess', 'Jonkheer', 'Col', 'Rev', 'Capt', 'Sir', 'Don'], ['Other', 'Miss', 'Miss', 'Miss', 'Mr', 'Mrs', 'Mrs', 'Other', 'Other', 'Other', 'Mr', 'Mr', 'Mr'], inplace=True)
pd.crosstab(train_df.Initial, train_df.Sex).T.style.background_gradient(cmap='gist_rainbow')
train_df.groupby('Initial')['Age'].mean()
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Mr'), 'Age'] = 32.5
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Mrs'), 'Age'] = 36
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Master'), 'Age'] = 4.5
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Miss'), 'Age'] = 22
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Other'), 'Age'] = 44.5
train_df['FamilySize'] = train_df['Parch'] + train_df['SibSp']
sns.pairplot(train_df, hue='Sex', palette='coolwarm') | code |
18117432/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.isnull().sum()
train_df['Initial'] = 0
for i in train_df:
train_df['Initial'] = train_df.Name.str.extract('([A-Za-z]+)\\.')
pd.crosstab(train_df.Initial, train_df.Sex).T.style.background_gradient(cmap='gist_rainbow')
train_df['Initial'].replace(['Dr', 'Mlle', 'Mme', 'Ms', 'Major', 'Lady', 'Countess', 'Jonkheer', 'Col', 'Rev', 'Capt', 'Sir', 'Don'], ['Other', 'Miss', 'Miss', 'Miss', 'Mr', 'Mrs', 'Mrs', 'Other', 'Other', 'Other', 'Mr', 'Mr', 'Mr'], inplace=True)
pd.crosstab(train_df.Initial, train_df.Sex).T.style.background_gradient(cmap='gist_rainbow')
train_df.groupby('Initial')['Age'].mean()
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Mr'), 'Age'] = 32.5
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Mrs'), 'Age'] = 36
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Master'), 'Age'] = 4.5
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Miss'), 'Age'] = 22
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Other'), 'Age'] = 44.5
sns.barplot(x='Sex', y='Survived', data=train_df) | code |
18117432/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
train_df.isnull().sum() | code |
18117432/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.isnull().sum()
train_df['Initial'] = 0
for i in train_df:
train_df['Initial'] = train_df.Name.str.extract('([A-Za-z]+)\\.')
pd.crosstab(train_df.Initial, train_df.Sex).T.style.background_gradient(cmap='gist_rainbow')
train_df['Initial'].replace(['Dr', 'Mlle', 'Mme', 'Ms', 'Major', 'Lady', 'Countess', 'Jonkheer', 'Col', 'Rev', 'Capt', 'Sir', 'Don'], ['Other', 'Miss', 'Miss', 'Miss', 'Mr', 'Mrs', 'Mrs', 'Other', 'Other', 'Other', 'Mr', 'Mr', 'Mr'], inplace=True)
pd.crosstab(train_df.Initial, train_df.Sex).T.style.background_gradient(cmap='gist_rainbow')
train_df.groupby('Initial')['Age'].mean()
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Mr'), 'Age'] = 32.5
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Mrs'), 'Age'] = 36
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Master'), 'Age'] = 4.5
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Miss'), 'Age'] = 22
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Other'), 'Age'] = 44.5
f, ax = plt.subplots()
train_df['Survived'].value_counts().plot.pie(explode=[0, 0.05], autopct='%1.1f%%', shadow=True)
ax.set_title('Survived')
ax.set_ylabel('')
plt.show() | code |
18117432/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.isnull().sum()
train_df['Initial'] = 0
for i in train_df:
train_df['Initial'] = train_df.Name.str.extract('([A-Za-z]+)\\.')
pd.crosstab(train_df.Initial, train_df.Sex).T.style.background_gradient(cmap='gist_rainbow')
train_df['Initial'].replace(['Dr', 'Mlle', 'Mme', 'Ms', 'Major', 'Lady', 'Countess', 'Jonkheer', 'Col', 'Rev', 'Capt', 'Sir', 'Don'], ['Other', 'Miss', 'Miss', 'Miss', 'Mr', 'Mrs', 'Mrs', 'Other', 'Other', 'Other', 'Mr', 'Mr', 'Mr'], inplace=True)
pd.crosstab(train_df.Initial, train_df.Sex).T.style.background_gradient(cmap='gist_rainbow')
train_df.groupby('Initial')['Age'].mean() | code |
18117432/cell_22 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.isnull().sum()
train_df['Initial'] = 0
for i in train_df:
train_df['Initial'] = train_df.Name.str.extract('([A-Za-z]+)\\.')
pd.crosstab(train_df.Initial, train_df.Sex).T.style.background_gradient(cmap='gist_rainbow')
train_df['Initial'].replace(['Dr', 'Mlle', 'Mme', 'Ms', 'Major', 'Lady', 'Countess', 'Jonkheer', 'Col', 'Rev', 'Capt', 'Sir', 'Don'], ['Other', 'Miss', 'Miss', 'Miss', 'Mr', 'Mrs', 'Mrs', 'Other', 'Other', 'Other', 'Mr', 'Mr', 'Mr'], inplace=True)
pd.crosstab(train_df.Initial, train_df.Sex).T.style.background_gradient(cmap='gist_rainbow')
train_df.groupby('Initial')['Age'].mean()
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Mr'), 'Age'] = 32.5
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Mrs'), 'Age'] = 36
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Master'), 'Age'] = 4.5
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Miss'), 'Age'] = 22
train_df.loc[train_df.Age.isnull() & (train_df.Initial == 'Other'), 'Age'] = 44.5
sns.factorplot(x='Embarked', y='Age', data=train_df, kind='bar', hue='Survived') | code |
18117432/cell_10 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.isnull().sum()
train_df['Initial'] = 0
for i in train_df:
train_df['Initial'] = train_df.Name.str.extract('([A-Za-z]+)\\.')
pd.crosstab(train_df.Initial, train_df.Sex).T.style.background_gradient(cmap='gist_rainbow') | code |
18117432/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.isnull().sum()
train_df['Initial'] = 0
for i in train_df:
train_df['Initial'] = train_df.Name.str.extract('([A-Za-z]+)\\.')
pd.crosstab(train_df.Initial, train_df.Sex).T.style.background_gradient(cmap='gist_rainbow')
train_df['Initial'].replace(['Dr', 'Mlle', 'Mme', 'Ms', 'Major', 'Lady', 'Countess', 'Jonkheer', 'Col', 'Rev', 'Capt', 'Sir', 'Don'], ['Other', 'Miss', 'Miss', 'Miss', 'Mr', 'Mrs', 'Mrs', 'Other', 'Other', 'Other', 'Mr', 'Mr', 'Mr'], inplace=True)
pd.crosstab(train_df.Initial, train_df.Sex).T.style.background_gradient(cmap='gist_rainbow') | code |
18117432/cell_5 | [
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
display(train_df.head())
print('Shape of Data : ', train_df.shape) | code |
2026799/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/akosombo.csv')
df.dtypes
df['eta'] = df.eta.map(lambda x: x.split(':')[-1])
df['percentage'] = df['percentage'].apply(lambda x: x.split('%')[0])
df['percentage'] = df['percentage'].astype(float)
df['size'] = df['size'].map(lambda x: x.split('KB')[0])
df['size'] = df['size'].astype(float)
df['eta'] = df['eta'].astype(float)
sns.jointplot(data=df, x='eta', y='size') | code |
2026799/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/akosombo.csv')
df.dtypes
df['eta'] = df.eta.map(lambda x: x.split(':')[-1])
df['percentage'] = df['percentage'].apply(lambda x: x.split('%')[0])
df['percentage'] = df['percentage'].astype(float)
df['size'] = df['size'].map(lambda x: x.split('KB')[0])
df['size'] = df['size'].astype(float)
df.head() | code |
2026799/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2026799/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/akosombo.csv')
df.dtypes
pd.isnull(df).any() | code |
2026799/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/akosombo.csv')
df.dtypes
df['eta'] = df.eta.map(lambda x: x.split(':')[-1])
df['percentage'] = df['percentage'].apply(lambda x: x.split('%')[0])
df['percentage'] = df['percentage'].astype(float)
df['size'] = df['size'].map(lambda x: x.split('KB')[0])
df['size'] = df['size'].astype(float)
df['eta'] = df['eta'].astype(float)
sns.jointplot(data=df, x='speed', y='size') | code |
2026799/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/akosombo.csv')
df.dtypes
df['eta'] = df.eta.map(lambda x: x.split(':')[-1])
df['percentage'] = df['percentage'].apply(lambda x: x.split('%')[0])
df['percentage'] = df['percentage'].astype(float)
df['size'] = df['size'].map(lambda x: x.split('KB')[0])
df['size'] = df['size'].astype(float)
df['eta'] = df['eta'].astype(float)
df[df['eta'] == 4] | code |
2026799/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/akosombo.csv')
df.describe() | code |
2026799/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/akosombo.csv')
df.dtypes
df['eta'] = df.eta.map(lambda x: x.split(':')[-1])
df['percentage'] = df['percentage'].apply(lambda x: x.split('%')[0])
df['percentage'] = df['percentage'].astype(float)
df['size'] = df['size'].map(lambda x: x.split('KB')[0])
df['size'] = df['size'].astype(float)
df['eta'] = df['eta'].astype(float)
df[df['size'] == 9010] | code |
2026799/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/akosombo.csv')
df.dtypes
df['eta'] = df.eta.map(lambda x: x.split(':')[-1])
df['percentage'] = df['percentage'].apply(lambda x: x.split('%')[0])
df['percentage'] = df['percentage'].astype(float)
df['size'] = df['size'].map(lambda x: x.split('KB')[0])
df['size'] = df['size'].astype(float)
df['eta'] = df['eta'].astype(float)
df.describe() | code |
2026799/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/akosombo.csv')
df.dtypes | code |
128024531/cell_42 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = user_counts[user_counts == 1].index.tolist()
recipesidss = reviews.loc[reviews['AuthorId'].isin(single_recipe_users), 'RecipeId']
recipe_counts = reviews.loc[reviews['RecipeId'].isin(recipesidss), 'RecipeId'].value_counts()
useless_recipes = recipe_counts[recipe_counts == 1].index.tolist()
reviews = reviews[~reviews['RecipeId'].isin(useless_recipes)]
recipes = recipes[~recipes['RecipeId'].isin(useless_recipes)]
recipes.isnull().sum(axis=0)
recipesReviews = recipes.loc[recipes['RecipeId'].isin(reviews['RecipeId'].unique())]
recipesReviews.nunique()
recipesReviews[recipesReviews.duplicated(subset=['Name', 'AuthorId'], keep=False)].sort_values(by='Name') | code |
128024531/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = user_counts[user_counts == 1].index.tolist()
recipesidss = reviews.loc[reviews['AuthorId'].isin(single_recipe_users), 'RecipeId']
recipe_counts = reviews.loc[reviews['RecipeId'].isin(recipesidss), 'RecipeId'].value_counts()
useless_recipes = recipe_counts[recipe_counts == 1].index.tolist()
recipes = recipes[~recipes['RecipeId'].isin(useless_recipes)]
len(recipes) | code |
128024531/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = user_counts[user_counts == 1].index.tolist()
recipesidss = reviews.loc[reviews['AuthorId'].isin(single_recipe_users), 'RecipeId']
recipe_counts = reviews.loc[reviews['RecipeId'].isin(recipesidss), 'RecipeId'].value_counts()
len(recipe_counts) | code |
128024531/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.head(4) | code |
128024531/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = user_counts[user_counts == 1].index.tolist()
recipesidss = reviews.loc[reviews['AuthorId'].isin(single_recipe_users), 'RecipeId']
recipe_counts = reviews.loc[reviews['RecipeId'].isin(recipesidss), 'RecipeId'].value_counts()
useless_recipes = recipe_counts[recipe_counts == 1].index.tolist()
reviews = reviews[~reviews['RecipeId'].isin(useless_recipes)]
recipes = recipes[~recipes['RecipeId'].isin(useless_recipes)]
recipes.isnull().sum(axis=0)
recipesReviews = recipes.loc[recipes['RecipeId'].isin(reviews['RecipeId'].unique())]
recipesNot_in_list = reviews.loc[~reviews['RecipeId'].isin(recipes['RecipeId'])]
len(reviews) | code |
128024531/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = user_counts[user_counts == 1].index.tolist()
recipesidss = reviews.loc[reviews['AuthorId'].isin(single_recipe_users), 'RecipeId']
recipe_counts = reviews.loc[reviews['RecipeId'].isin(recipesidss), 'RecipeId'].value_counts()
useless_recipes = recipe_counts[recipe_counts == 1].index.tolist()
reviews = reviews[~reviews['RecipeId'].isin(useless_recipes)]
recipes = recipes[~recipes['RecipeId'].isin(useless_recipes)]
recipes.isnull().sum(axis=0)
recipesReviews = recipes.loc[recipes['RecipeId'].isin(reviews['RecipeId'].unique())]
recipesNot_in_list = reviews.loc[~reviews['RecipeId'].isin(recipes['RecipeId'])]
recipesNot_in_list.head(5) | code |
128024531/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = user_counts[user_counts == 1].index.tolist()
recipesidss = reviews.loc[reviews['AuthorId'].isin(single_recipe_users), 'RecipeId']
recipe_counts = reviews.loc[reviews['RecipeId'].isin(recipesidss), 'RecipeId'].value_counts()
useless_recipes = recipe_counts[recipe_counts == 1].index.tolist()
reviews = reviews[~reviews['RecipeId'].isin(useless_recipes)]
recipes = recipes[~recipes['RecipeId'].isin(useless_recipes)]
recipes.isnull().sum(axis=0)
recipesReviews = recipes.loc[recipes['RecipeId'].isin(reviews['RecipeId'].unique())]
checkingForRecipe = recipes.loc[recipes['RecipeId'] == 194165]
checkingForRecipe | code |
128024531/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
recipes['Name'].nunique() | code |
128024531/cell_40 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = user_counts[user_counts == 1].index.tolist()
recipesidss = reviews.loc[reviews['AuthorId'].isin(single_recipe_users), 'RecipeId']
recipe_counts = reviews.loc[reviews['RecipeId'].isin(recipesidss), 'RecipeId'].value_counts()
useless_recipes = recipe_counts[recipe_counts == 1].index.tolist()
reviews = reviews[~reviews['RecipeId'].isin(useless_recipes)]
recipes = recipes[~recipes['RecipeId'].isin(useless_recipes)]
recipes.isnull().sum(axis=0)
recipesReviews = recipes.loc[recipes['RecipeId'].isin(reviews['RecipeId'].unique())]
recipesReviews.nunique() | code |
128024531/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = user_counts[user_counts == 1].index.tolist()
recipesidss = reviews.loc[reviews['AuthorId'].isin(single_recipe_users), 'RecipeId']
recipe_counts = reviews.loc[reviews['RecipeId'].isin(recipesidss), 'RecipeId'].value_counts()
useless_recipes = recipe_counts[recipe_counts == 1].index.tolist()
reviews = reviews[~reviews['RecipeId'].isin(useless_recipes)]
recipes = recipes[~recipes['RecipeId'].isin(useless_recipes)]
recipes.isnull().sum(axis=0)
recipesReviews = recipes.loc[recipes['RecipeId'].isin(reviews['RecipeId'].unique())]
recipesNot_in_list = reviews.loc[~reviews['RecipeId'].isin(recipes['RecipeId'])]
len(recipesNot_in_list) | code |
128024531/cell_39 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = user_counts[user_counts == 1].index.tolist()
recipesidss = reviews.loc[reviews['AuthorId'].isin(single_recipe_users), 'RecipeId']
recipe_counts = reviews.loc[reviews['RecipeId'].isin(recipesidss), 'RecipeId'].value_counts()
useless_recipes = recipe_counts[recipe_counts == 1].index.tolist()
reviews = reviews[~reviews['RecipeId'].isin(useless_recipes)]
recipes = recipes[~recipes['RecipeId'].isin(useless_recipes)]
recipes.isnull().sum(axis=0)
recipesReviews = recipes.loc[recipes['RecipeId'].isin(reviews['RecipeId'].unique())]
len(recipesReviews) | code |
128024531/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = user_counts[user_counts == 1].index.tolist()
recipesidss = reviews.loc[reviews['AuthorId'].isin(single_recipe_users), 'RecipeId']
recipe_counts = reviews.loc[reviews['RecipeId'].isin(recipesidss), 'RecipeId'].value_counts()
useless_recipes = recipe_counts[recipe_counts == 1].index.tolist()
reviews = reviews[~reviews['RecipeId'].isin(useless_recipes)]
recipes = recipes[~recipes['RecipeId'].isin(useless_recipes)]
recipes.isnull().sum(axis=0)
recipesReviews = recipes.loc[recipes['RecipeId'].isin(reviews['RecipeId'].unique())]
len(recipesReviews) | code |
128024531/cell_41 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = user_counts[user_counts == 1].index.tolist()
recipesidss = reviews.loc[reviews['AuthorId'].isin(single_recipe_users), 'RecipeId']
recipe_counts = reviews.loc[reviews['RecipeId'].isin(recipesidss), 'RecipeId'].value_counts()
useless_recipes = recipe_counts[recipe_counts == 1].index.tolist()
reviews = reviews[~reviews['RecipeId'].isin(useless_recipes)]
recipes = recipes[~recipes['RecipeId'].isin(useless_recipes)]
recipes.isnull().sum(axis=0)
recipesReviews = recipes.loc[recipes['RecipeId'].isin(reviews['RecipeId'].unique())]
recipesReviews.nunique()
recipesReviews[recipesReviews['Name'].duplicated(keep=False)].sort_values(by='Name').head(10) | code |
128024531/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
len(recipes) | code |
128024531/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
128024531/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
reviews['RecipeId'].nunique() | code |
128024531/cell_45 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = user_counts[user_counts == 1].index.tolist()
recipesidss = reviews.loc[reviews['AuthorId'].isin(single_recipe_users), 'RecipeId']
recipe_counts = reviews.loc[reviews['RecipeId'].isin(recipesidss), 'RecipeId'].value_counts()
useless_recipes = recipe_counts[recipe_counts == 1].index.tolist()
reviews = reviews[~reviews['RecipeId'].isin(useless_recipes)]
recipes = recipes[~recipes['RecipeId'].isin(useless_recipes)]
recipes.isnull().sum(axis=0)
recipesReviews = recipes.loc[recipes['RecipeId'].isin(reviews['RecipeId'].unique())]
recipesNot_in_list = reviews.loc[~reviews['RecipeId'].isin(recipes['RecipeId'])]
reviews = reviews.loc[~reviews['ReviewId'].isin(recipesNot_in_list['ReviewId'])]
reviews.isnull().sum(axis=0)
recipesReviews.nunique()
recipesReviews[recipesReviews.duplicated(subset=['Name', 'AuthorId'], keep=False)].sort_values(by='Name')
duplicates = recipesReviews[recipesReviews.duplicated(subset=['Name', 'AuthorId'], keep=False)]
dropped_duplicates_df = duplicates.drop_duplicates(subset=['Name', 'AuthorId'], keep='first')
temp2 = reviews.loc[reviews['RecipeId'].isin(duplicates['RecipeId'])]
temp2['RecipeId'].value_counts() | code |
128024531/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = user_counts[user_counts == 1].index.tolist()
recipesidss = reviews.loc[reviews['AuthorId'].isin(single_recipe_users), 'RecipeId']
recipe_counts = reviews.loc[reviews['RecipeId'].isin(recipesidss), 'RecipeId'].value_counts()
useless_recipes = recipe_counts[recipe_counts == 1].index.tolist()
reviews = reviews[~reviews['RecipeId'].isin(useless_recipes)]
len(reviews) | code |
128024531/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = user_counts[user_counts == 1].index.tolist()
recipesidss = reviews.loc[reviews['AuthorId'].isin(single_recipe_users), 'RecipeId']
recipe_counts = reviews.loc[reviews['RecipeId'].isin(recipesidss), 'RecipeId'].value_counts()
useless_recipes = recipe_counts[recipe_counts == 1].index.tolist()
len(useless_recipes) | code |
128024531/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = user_counts[user_counts == 1].index.tolist()
recipesidss = reviews.loc[reviews['AuthorId'].isin(single_recipe_users), 'RecipeId']
recipe_counts = reviews.loc[reviews['RecipeId'].isin(recipesidss), 'RecipeId'].value_counts()
len(reviews) | code |
128024531/cell_38 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = user_counts[user_counts == 1].index.tolist()
recipesidss = reviews.loc[reviews['AuthorId'].isin(single_recipe_users), 'RecipeId']
recipe_counts = reviews.loc[reviews['RecipeId'].isin(recipesidss), 'RecipeId'].value_counts()
useless_recipes = recipe_counts[recipe_counts == 1].index.tolist()
reviews = reviews[~reviews['RecipeId'].isin(useless_recipes)]
recipes = recipes[~recipes['RecipeId'].isin(useless_recipes)]
recipes.isnull().sum(axis=0)
recipesReviews = recipes.loc[recipes['RecipeId'].isin(reviews['RecipeId'].unique())]
recipesReviews.head(5) | code |
128024531/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
reviews.head(4) | code |
128024531/cell_46 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = user_counts[user_counts == 1].index.tolist()
recipesidss = reviews.loc[reviews['AuthorId'].isin(single_recipe_users), 'RecipeId']
recipe_counts = reviews.loc[reviews['RecipeId'].isin(recipesidss), 'RecipeId'].value_counts()
useless_recipes = recipe_counts[recipe_counts == 1].index.tolist()
reviews = reviews[~reviews['RecipeId'].isin(useless_recipes)]
recipes = recipes[~recipes['RecipeId'].isin(useless_recipes)]
recipes.isnull().sum(axis=0)
recipesReviews = recipes.loc[recipes['RecipeId'].isin(reviews['RecipeId'].unique())]
recipesNot_in_list = reviews.loc[~reviews['RecipeId'].isin(recipes['RecipeId'])]
reviews = reviews.loc[~reviews['ReviewId'].isin(recipesNot_in_list['ReviewId'])]
reviews.isnull().sum(axis=0)
recipesReviews.nunique()
recipesReviews[recipesReviews.duplicated(subset=['Name', 'AuthorId'], keep=False)].sort_values(by='Name')
duplicates = recipesReviews[recipesReviews.duplicated(subset=['Name', 'AuthorId'], keep=False)]
dropped_duplicates_df = duplicates.drop_duplicates(subset=['Name', 'AuthorId'], keep='first')
temp2 = reviews.loc[reviews['RecipeId'].isin(duplicates['RecipeId'])]
temp2 | code |
128024531/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = user_counts[user_counts == 1].index.tolist()
recipesidss = reviews.loc[reviews['AuthorId'].isin(single_recipe_users), 'RecipeId']
recipe_counts = reviews.loc[reviews['RecipeId'].isin(recipesidss), 'RecipeId'].value_counts()
useless_recipes = recipe_counts[recipe_counts == 1].index.tolist()
recipes = recipes[~recipes['RecipeId'].isin(useless_recipes)]
recipes.isnull().sum(axis=0) | code |
128024531/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = user_counts[user_counts == 1].index.tolist()
recipesidss = reviews.loc[reviews['AuthorId'].isin(single_recipe_users), 'RecipeId']
recipe_counts = reviews.loc[reviews['RecipeId'].isin(recipesidss), 'RecipeId'].value_counts()
useless_recipes = recipe_counts[recipe_counts == 1].index.tolist()
reviews = reviews[~reviews['RecipeId'].isin(useless_recipes)]
recipes = recipes[~recipes['RecipeId'].isin(useless_recipes)]
recipes.isnull().sum(axis=0)
recipesReviews = recipes.loc[recipes['RecipeId'].isin(reviews['RecipeId'].unique())]
recipesNot_in_list = reviews.loc[~reviews['RecipeId'].isin(recipes['RecipeId'])]
reviews = reviews.loc[~reviews['ReviewId'].isin(recipesNot_in_list['ReviewId'])]
reviews.isnull().sum(axis=0) | code |
128024531/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = user_counts[user_counts == 1].index.tolist()
recipesidss = reviews.loc[reviews['AuthorId'].isin(single_recipe_users), 'RecipeId']
recipe_counts = reviews.loc[reviews['RecipeId'].isin(recipesidss), 'RecipeId'].value_counts()
recipe_counts.head(5) | code |
128024531/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns | code |
128024531/cell_36 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = user_counts[user_counts == 1].index.tolist()
recipesidss = reviews.loc[reviews['AuthorId'].isin(single_recipe_users), 'RecipeId']
recipe_counts = reviews.loc[reviews['RecipeId'].isin(recipesidss), 'RecipeId'].value_counts()
useless_recipes = recipe_counts[recipe_counts == 1].index.tolist()
reviews = reviews[~reviews['RecipeId'].isin(useless_recipes)]
recipes = recipes[~recipes['RecipeId'].isin(useless_recipes)]
recipes.isnull().sum(axis=0)
recipesReviews = recipes.loc[recipes['RecipeId'].isin(reviews['RecipeId'].unique())]
recipesNot_in_list = reviews.loc[~reviews['RecipeId'].isin(recipes['RecipeId'])]
reviews = reviews.loc[~reviews['ReviewId'].isin(recipesNot_in_list['ReviewId'])]
len(reviews) | code |
32070986/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/imet-2020-fgvc7/train.csv')
train_df['attribute_ids'] = train_df['attribute_ids'].apply(lambda x: x.split(' '))
train_df['id'] = train_df['id'].apply(lambda x: x + '.png')
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
train_df_d = pd.DataFrame(mlb.fit_transform(train_df['attribute_ids']), columns=mlb.classes_, index=train_df.index)
label_names = train_df_d.columns
sam_sub_df = pd.read_csv('../input/imet-2020-fgvc7/sample_submission.csv')
sam_sub_df['id'] = sam_sub_df['id'].apply(lambda x: x + '.png')
probs.shape
threshold = probs[0].mean()
labels_01 = (probs > threshold).astype(np.int)
labels_01
labels_01.shape
sub = pd.DataFrame(labels_01, columns=label_names)
sam_sub_df['id'] = sam_sub_df['id'].str[:-4]
sam_sub_df['attribute_ids'] = sub['attribute_ids']
sam_sub_df.head() | code |
32070986/cell_13 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | probs.shape | code |
32070986/cell_4 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
import pandas as pd
train_df = pd.read_csv('../input/imet-2020-fgvc7/train.csv')
train_df['attribute_ids'] = train_df['attribute_ids'].apply(lambda x: x.split(' '))
train_df['id'] = train_df['id'].apply(lambda x: x + '.png')
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
train_df_d = pd.DataFrame(mlb.fit_transform(train_df['attribute_ids']), columns=mlb.classes_, index=train_df.index)
print(train_df_d.shape)
train_df_d.head() | code |
32070986/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
import pandas as pd
train_df = pd.read_csv('../input/imet-2020-fgvc7/train.csv')
train_df['attribute_ids'] = train_df['attribute_ids'].apply(lambda x: x.split(' '))
train_df['id'] = train_df['id'].apply(lambda x: x + '.png')
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
train_df_d = pd.DataFrame(mlb.fit_transform(train_df['attribute_ids']), columns=mlb.classes_, index=train_df.index)
sam_sub_df = pd.read_csv('../input/imet-2020-fgvc7/sample_submission.csv')
sam_sub_df['id'] = sam_sub_df['id'].apply(lambda x: x + '.png')
sam_sub_df['id'] = sam_sub_df['id'].str[:-4]
sam_sub_df.head() | code |
32070986/cell_2 | [
"text_plain_output_1.png"
] | import sys
import numpy as np
import pandas as pd
import os
import sys
import tensorflow as tf, tensorflow.keras.backend as K
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from matplotlib import pyplot as plt
sys.path.insert(0, '/kaggle/input/efficientnet-keras-source-code/')
import efficientnet.tfkeras as efn
print(tf.__version__)
print(tf.keras.__version__) | code |
32070986/cell_11 | [
"text_plain_output_1.png"
] | test_datagen = ImageDataGenerator(rescale=1.0 / 255)
test_generator = test_datagen.flow_from_dataframe(dataframe=sam_sub_df, directory='../input/imet-2020-fgvc7/test', x_col='id', target_size=(img_size, img_size), batch_size=1, shuffle=False, class_mode=None) | code |
32070986/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/imet-2020-fgvc7/train.csv')
train_df['attribute_ids'] = train_df['attribute_ids'].apply(lambda x: x.split(' '))
train_df['id'] = train_df['id'].apply(lambda x: x + '.png')
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
train_df_d = pd.DataFrame(mlb.fit_transform(train_df['attribute_ids']), columns=mlb.classes_, index=train_df.index)
label_names = train_df_d.columns
sam_sub_df = pd.read_csv('../input/imet-2020-fgvc7/sample_submission.csv')
sam_sub_df['id'] = sam_sub_df['id'].apply(lambda x: x + '.png')
probs.shape
threshold = probs[0].mean()
labels_01 = (probs > threshold).astype(np.int)
labels_01
labels_01.shape
sub = pd.DataFrame(labels_01, columns=label_names)
sub.head() | code |
32070986/cell_7 | [
"text_html_output_1.png"
] | import gc
import gc
del train_df_d
gc.collect() | code |
32070986/cell_18 | [
"text_plain_output_1.png"
] | sub['attribute_ids'] = ''
for col_name in sub.columns:
sub.ix[sub[col_name] == 1, 'attribute_ids'] = sub['attribute_ids'] + ' ' + col_name | code |
32070986/cell_8 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
import pandas as pd
train_df = pd.read_csv('../input/imet-2020-fgvc7/train.csv')
train_df['attribute_ids'] = train_df['attribute_ids'].apply(lambda x: x.split(' '))
train_df['id'] = train_df['id'].apply(lambda x: x + '.png')
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
train_df_d = pd.DataFrame(mlb.fit_transform(train_df['attribute_ids']), columns=mlb.classes_, index=train_df.index)
sam_sub_df = pd.read_csv('../input/imet-2020-fgvc7/sample_submission.csv')
sam_sub_df['id'] = sam_sub_df['id'].apply(lambda x: x + '.png')
print(sam_sub_df.shape)
sam_sub_df.head() | code |
32070986/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
probs.shape
threshold = probs[0].mean()
labels_01 = (probs > threshold).astype(np.int)
labels_01 | code |
32070986/cell_16 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
probs.shape
threshold = probs[0].mean()
labels_01 = (probs > threshold).astype(np.int)
labels_01
labels_01.shape | code |
32070986/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/imet-2020-fgvc7/train.csv')
train_df['attribute_ids'] = train_df['attribute_ids'].apply(lambda x: x.split(' '))
train_df['id'] = train_df['id'].apply(lambda x: x + '.png')
print(train_df.shape)
train_df.head() | code |
32070986/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/imet-2020-fgvc7/train.csv')
train_df['attribute_ids'] = train_df['attribute_ids'].apply(lambda x: x.split(' '))
train_df['id'] = train_df['id'].apply(lambda x: x + '.png')
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
train_df_d = pd.DataFrame(mlb.fit_transform(train_df['attribute_ids']), columns=mlb.classes_, index=train_df.index)
label_names = train_df_d.columns
sam_sub_df = pd.read_csv('../input/imet-2020-fgvc7/sample_submission.csv')
sam_sub_df['id'] = sam_sub_df['id'].apply(lambda x: x + '.png')
probs.shape
threshold = probs[0].mean()
labels_01 = (probs > threshold).astype(np.int)
labels_01
labels_01.shape
sub = pd.DataFrame(labels_01, columns=label_names)
print(sub.shape)
sub.head() | code |
32070986/cell_14 | [
"text_html_output_1.png"
] | probs.shape
probs[0].mean() | code |
32070986/cell_22 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/imet-2020-fgvc7/train.csv')
train_df['attribute_ids'] = train_df['attribute_ids'].apply(lambda x: x.split(' '))
train_df['id'] = train_df['id'].apply(lambda x: x + '.png')
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
train_df_d = pd.DataFrame(mlb.fit_transform(train_df['attribute_ids']), columns=mlb.classes_, index=train_df.index)
label_names = train_df_d.columns
sam_sub_df = pd.read_csv('../input/imet-2020-fgvc7/sample_submission.csv')
sam_sub_df['id'] = sam_sub_df['id'].apply(lambda x: x + '.png')
probs.shape
threshold = probs[0].mean()
labels_01 = (probs > threshold).astype(np.int)
labels_01
labels_01.shape
sub = pd.DataFrame(labels_01, columns=label_names)
sam_sub_df['id'] = sam_sub_df['id'].str[:-4]
sam_sub_df['attribute_ids'] = sub['attribute_ids']
sam_sub_df.tail() | code |
32070986/cell_12 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | test_generator.reset()
probs = model.predict_generator(test_generator, steps=len(test_generator.filenames)) | code |
32070986/cell_5 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
import pandas as pd
train_df = pd.read_csv('../input/imet-2020-fgvc7/train.csv')
train_df['attribute_ids'] = train_df['attribute_ids'].apply(lambda x: x.split(' '))
train_df['id'] = train_df['id'].apply(lambda x: x + '.png')
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
train_df_d = pd.DataFrame(mlb.fit_transform(train_df['attribute_ids']), columns=mlb.classes_, index=train_df.index)
train_df_d[:1][['448', '2429', '782']] | code |
90147004/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import seaborn as sns
import plotly.express as px
import time
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
mse = mean_squared_error
def downcast(df: pd.DataFrame) -> pd.DataFrame:
float_cols = [c for c in df if df[c].dtype in ['float64']]
int_cols = [c for c in df if df[c].dtype in ['int64']]
df[float_cols] = df[float_cols].astype('float32')
df[int_cols] = df[int_cols].astype('int16')
return df
def lag_feature(df: pd.DataFrame, lag: int, col: str, merge_cols, fill_value=-10, suffix=''):
temp = df[merge_cols + [col]]
temp = temp.groupby(merge_cols).agg({f'{col}': 'mean'}).reset_index()
new_col_name = f'{col}{suffix}_lag{lag}'
temp.columns = merge_cols + [new_col_name]
temp['date_block_num'] += lag
if new_col_name not in df.columns:
df = pd.merge(df, temp, on=merge_cols, how='left')
temp = None
df[new_col_name] = df[new_col_name].fillna(fill_value).astype('float32')
return (df, new_col_name)
items = pd.read_csv('../input/data-preprocessing/items.csv')
shops = pd.read_csv('../input/data-preprocessing/shops.csv')
cats = pd.read_csv('../input/data-preprocessing/item_categories.csv')
train = pd.read_csv('../input/data-preprocessing/sales_train.csv')
test = pd.read_csv('../input/data-preprocessing/test.csv').set_index('ID')
dataframes = [train, shops, items, cats]
for d in dataframes:
d = downcast(d)
tme = pd.read_csv('../input/data-preprocessing/train_monthly_extended.csv')
tme = downcast(tme)
cast_cols = ['item_cnt_month', 'days_with_sales', 'date_block_num']
tme[cast_cols] = tme[cast_cols].astype(int)
tme['item_cnt_month'] = tme['item_cnt_month'].clip(0, 50)
tme.sample(4) | code |
90147004/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import time
import numpy as np
import pandas as pd
import os
import seaborn as sns
import plotly.express as px
import time
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
mse = mean_squared_error
def downcast(df: pd.DataFrame) -> pd.DataFrame:
float_cols = [c for c in df if df[c].dtype in ['float64']]
int_cols = [c for c in df if df[c].dtype in ['int64']]
df[float_cols] = df[float_cols].astype('float32')
df[int_cols] = df[int_cols].astype('int16')
return df
def lag_feature(df: pd.DataFrame, lag: int, col: str, merge_cols, fill_value=-10, suffix=''):
temp = df[merge_cols + [col]]
temp = temp.groupby(merge_cols).agg({f'{col}': 'mean'}).reset_index()
new_col_name = f'{col}{suffix}_lag{lag}'
temp.columns = merge_cols + [new_col_name]
temp['date_block_num'] += lag
if new_col_name not in df.columns:
df = pd.merge(df, temp, on=merge_cols, how='left')
temp = None
df[new_col_name] = df[new_col_name].fillna(fill_value).astype('float32')
return (df, new_col_name)
items = pd.read_csv('../input/data-preprocessing/items.csv')
shops = pd.read_csv('../input/data-preprocessing/shops.csv')
cats = pd.read_csv('../input/data-preprocessing/item_categories.csv')
train = pd.read_csv('../input/data-preprocessing/sales_train.csv')
test = pd.read_csv('../input/data-preprocessing/test.csv').set_index('ID')
dataframes = [train, shops, items, cats]
for d in dataframes:
d = downcast(d)
tme = pd.read_csv('../input/data-preprocessing/train_monthly_extended.csv')
tme = downcast(tme)
cast_cols = ['item_cnt_month', 'days_with_sales', 'date_block_num']
tme[cast_cols] = tme[cast_cols].astype(int)
tme['item_cnt_month'] = tme['item_cnt_month'].clip(0, 50)
tme.sample(4)
print('> Building features based on the lags of item_cnt_month')
cols = []
for lag in [1, 2, 3, 4, 6, 12]:
t = time.process_time()
print(f'Processing lag {lag} - filling strategy is for decision trees')
tme, new_col = lag_feature(tme, lag, 'item_cnt_month', ['date_block_num', 'shop_id', 'item_id'], fill_value=np.nan)
elapsed_time = time.process_time() - t
print(f' -- {new_col} took {round(elapsed_time, 1)}')
cols.append(new_col)
print('> Building (renaming) target')
tme, new_col = lag_feature(tme, -1, 'item_cnt_month', ['date_block_num', 'shop_id', 'item_id'], fill_value=0)
tme = tme.rename(columns={new_col: 'target'}) | code |
90147004/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import time
import numpy as np
import pandas as pd
import os
import seaborn as sns
import plotly.express as px
import time
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
mse = mean_squared_error
def downcast(df: pd.DataFrame) -> pd.DataFrame:
float_cols = [c for c in df if df[c].dtype in ['float64']]
int_cols = [c for c in df if df[c].dtype in ['int64']]
df[float_cols] = df[float_cols].astype('float32')
df[int_cols] = df[int_cols].astype('int16')
return df
def lag_feature(df: pd.DataFrame, lag: int, col: str, merge_cols, fill_value=-10, suffix=''):
temp = df[merge_cols + [col]]
temp = temp.groupby(merge_cols).agg({f'{col}': 'mean'}).reset_index()
new_col_name = f'{col}{suffix}_lag{lag}'
temp.columns = merge_cols + [new_col_name]
temp['date_block_num'] += lag
if new_col_name not in df.columns:
df = pd.merge(df, temp, on=merge_cols, how='left')
temp = None
df[new_col_name] = df[new_col_name].fillna(fill_value).astype('float32')
return (df, new_col_name)
items = pd.read_csv('../input/data-preprocessing/items.csv')
shops = pd.read_csv('../input/data-preprocessing/shops.csv')
cats = pd.read_csv('../input/data-preprocessing/item_categories.csv')
train = pd.read_csv('../input/data-preprocessing/sales_train.csv')
test = pd.read_csv('../input/data-preprocessing/test.csv').set_index('ID')
dataframes = [train, shops, items, cats]
for d in dataframes:
d = downcast(d)
tme = pd.read_csv('../input/data-preprocessing/train_monthly_extended.csv')
tme = downcast(tme)
cast_cols = ['item_cnt_month', 'days_with_sales', 'date_block_num']
tme[cast_cols] = tme[cast_cols].astype(int)
tme['item_cnt_month'] = tme['item_cnt_month'].clip(0, 50)
tme.sample(4)
cols = []
for lag in [1, 2, 3, 4, 6, 12]:
t = time.process_time()
tme, new_col = lag_feature(tme, lag, 'item_cnt_month', ['date_block_num', 'shop_id', 'item_id'], fill_value=np.nan)
elapsed_time = time.process_time() - t
cols.append(new_col)
tme, new_col = lag_feature(tme, -1, 'item_cnt_month', ['date_block_num', 'shop_id', 'item_id'], fill_value=0)
tme = tme.rename(columns={new_col: 'target'})
for lag in [1, 2, 3, 4, 12]:
t = time.process_time()
print(f'Processing lag {lag} - filling strategy is for decision trees')
tme, new_col = lag_feature(tme, lag, 'item_cnt_month', ['date_block_num', 'shop_id'], fill_value=np.nan, suffix='s')
elapsed_time = time.process_time() - t
print(f' -- {new_col} took {round(elapsed_time, 1)}')
cols.append(new_col) | code |
90147004/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import time
import numpy as np
import pandas as pd
import os
import seaborn as sns
import plotly.express as px
import time
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
mse = mean_squared_error
def downcast(df: pd.DataFrame) -> pd.DataFrame:
float_cols = [c for c in df if df[c].dtype in ['float64']]
int_cols = [c for c in df if df[c].dtype in ['int64']]
df[float_cols] = df[float_cols].astype('float32')
df[int_cols] = df[int_cols].astype('int16')
return df
def lag_feature(df: pd.DataFrame, lag: int, col: str, merge_cols, fill_value=-10, suffix=''):
temp = df[merge_cols + [col]]
temp = temp.groupby(merge_cols).agg({f'{col}': 'mean'}).reset_index()
new_col_name = f'{col}{suffix}_lag{lag}'
temp.columns = merge_cols + [new_col_name]
temp['date_block_num'] += lag
if new_col_name not in df.columns:
df = pd.merge(df, temp, on=merge_cols, how='left')
temp = None
df[new_col_name] = df[new_col_name].fillna(fill_value).astype('float32')
return (df, new_col_name)
items = pd.read_csv('../input/data-preprocessing/items.csv')
shops = pd.read_csv('../input/data-preprocessing/shops.csv')
cats = pd.read_csv('../input/data-preprocessing/item_categories.csv')
train = pd.read_csv('../input/data-preprocessing/sales_train.csv')
test = pd.read_csv('../input/data-preprocessing/test.csv').set_index('ID')
dataframes = [train, shops, items, cats]
for d in dataframes:
d = downcast(d)
tme = pd.read_csv('../input/data-preprocessing/train_monthly_extended.csv')
tme = downcast(tme)
cast_cols = ['item_cnt_month', 'days_with_sales', 'date_block_num']
tme[cast_cols] = tme[cast_cols].astype(int)
tme['item_cnt_month'] = tme['item_cnt_month'].clip(0, 50)
tme.sample(4)
cols = []
for lag in [1, 2, 3, 4, 6, 12]:
t = time.process_time()
tme, new_col = lag_feature(tme, lag, 'item_cnt_month', ['date_block_num', 'shop_id', 'item_id'], fill_value=np.nan)
elapsed_time = time.process_time() - t
cols.append(new_col)
tme, new_col = lag_feature(tme, -1, 'item_cnt_month', ['date_block_num', 'shop_id', 'item_id'], fill_value=0)
tme = tme.rename(columns={new_col: 'target'})
for lag in [1, 2, 3, 4, 12]:
t = time.process_time()
tme, new_col = lag_feature(tme, lag, 'item_cnt_month', ['date_block_num', 'shop_id'], fill_value=np.nan, suffix='s')
elapsed_time = time.process_time() - t
cols.append(new_col)
for lag in [1, 2, 3, 4, 12]:
t = time.process_time()
print(f'Processing lag {lag} - filling strategy is for decision trees')
tme, new_col = lag_feature(tme, lag, 'item_cnt_month', ['date_block_num', 'item_id'], fill_value=np.nan, suffix='i')
elapsed_time = time.process_time() - t
print(f' -- {new_col} took {round(elapsed_time, 1)}')
cols.append(new_col) | code |
90147004/cell_3 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import seaborn as sns
import plotly.express as px
import time
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
mse = mean_squared_error
def downcast(df: pd.DataFrame) -> pd.DataFrame:
float_cols = [c for c in df if df[c].dtype in ['float64']]
int_cols = [c for c in df if df[c].dtype in ['int64']]
df[float_cols] = df[float_cols].astype('float32')
df[int_cols] = df[int_cols].astype('int16')
return df
def lag_feature(df: pd.DataFrame, lag: int, col: str, merge_cols, fill_value=-10, suffix=''):
temp = df[merge_cols + [col]]
temp = temp.groupby(merge_cols).agg({f'{col}': 'mean'}).reset_index()
new_col_name = f'{col}{suffix}_lag{lag}'
temp.columns = merge_cols + [new_col_name]
temp['date_block_num'] += lag
if new_col_name not in df.columns:
df = pd.merge(df, temp, on=merge_cols, how='left')
temp = None
df[new_col_name] = df[new_col_name].fillna(fill_value).astype('float32')
return (df, new_col_name)
items = pd.read_csv('../input/data-preprocessing/items.csv')
shops = pd.read_csv('../input/data-preprocessing/shops.csv')
cats = pd.read_csv('../input/data-preprocessing/item_categories.csv')
train = pd.read_csv('../input/data-preprocessing/sales_train.csv')
test = pd.read_csv('../input/data-preprocessing/test.csv').set_index('ID')
dataframes = [train, shops, items, cats]
for d in dataframes:
d = downcast(d)
train.sample(3) | code |
90147004/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from nltk.stem import WordNetLemmatizer, SnowballStemmer
from nltk.tokenize import RegexpTokenizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import mean_squared_error
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import seaborn as sns
import plotly.express as px
import time
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
mse = mean_squared_error
def downcast(df: pd.DataFrame) -> pd.DataFrame:
float_cols = [c for c in df if df[c].dtype in ['float64']]
int_cols = [c for c in df if df[c].dtype in ['int64']]
df[float_cols] = df[float_cols].astype('float32')
df[int_cols] = df[int_cols].astype('int16')
return df
def lag_feature(df: pd.DataFrame, lag: int, col: str, merge_cols, fill_value=-10, suffix=''):
temp = df[merge_cols + [col]]
temp = temp.groupby(merge_cols).agg({f'{col}': 'mean'}).reset_index()
new_col_name = f'{col}{suffix}_lag{lag}'
temp.columns = merge_cols + [new_col_name]
temp['date_block_num'] += lag
if new_col_name not in df.columns:
df = pd.merge(df, temp, on=merge_cols, how='left')
temp = None
df[new_col_name] = df[new_col_name].fillna(fill_value).astype('float32')
return (df, new_col_name)
items = pd.read_csv('../input/data-preprocessing/items.csv')
shops = pd.read_csv('../input/data-preprocessing/shops.csv')
cats = pd.read_csv('../input/data-preprocessing/item_categories.csv')
train = pd.read_csv('../input/data-preprocessing/sales_train.csv')
test = pd.read_csv('../input/data-preprocessing/test.csv').set_index('ID')
dataframes = [train, shops, items, cats]
for d in dataframes:
d = downcast(d)
tme = pd.read_csv('../input/data-preprocessing/train_monthly_extended.csv')
tme = downcast(tme)
cast_cols = ['item_cnt_month', 'days_with_sales', 'date_block_num']
tme[cast_cols] = tme[cast_cols].astype(int)
tme['item_cnt_month'] = tme['item_cnt_month'].clip(0, 50)
tme.sample(4)
import nltk
from nltk.stem import WordNetLemmatizer, SnowballStemmer
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords
stop_words = ['per', 'I', 'me', 'the', 'what', 'which', 'having', 'for', 'with', 'of', 'about', 'but', 'if', 'both', 'each', 'any', 'a']
stemmer = SnowballStemmer('english')
custom_tokenizer = RegexpTokenizer('\\w+')
def manipulate_str(a):
a = a.lower()
word_list = custom_tokenizer.tokenize(a)
stemmed_words = list()
for w in word_list:
sw = stemmer.stem(w)
if w not in stop_words and len(sw) > 2:
stemmed_words.append(sw)
return ' '.join(set(stemmed_words))
items['item_name_en_tokenized'] = items.item_name_en.apply(lambda x: manipulate_str(x))
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(min_df=15, max_features=40, stop_words='english')
vectorizer.fit(items['item_name_en_tokenized'])
text_features = vectorizer.transform(items['item_name_en_tokenized'])
text_features.shape
col_names = [f'txt_{c}' for c in vectorizer.get_feature_names_out()]
if type(text_features) is not pd.DataFrame:
text_features = pd.DataFrame.sparse.from_spmatrix(text_features, columns=col_names)
text_features.mean() | code |
90147004/cell_14 | [
"text_html_output_1.png"
] | from nltk.stem import WordNetLemmatizer, SnowballStemmer
from nltk.tokenize import RegexpTokenizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import mean_squared_error
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import seaborn as sns
import plotly.express as px
import time
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
mse = mean_squared_error
def downcast(df: pd.DataFrame) -> pd.DataFrame:
float_cols = [c for c in df if df[c].dtype in ['float64']]
int_cols = [c for c in df if df[c].dtype in ['int64']]
df[float_cols] = df[float_cols].astype('float32')
df[int_cols] = df[int_cols].astype('int16')
return df
def lag_feature(df: pd.DataFrame, lag: int, col: str, merge_cols, fill_value=-10, suffix=''):
temp = df[merge_cols + [col]]
temp = temp.groupby(merge_cols).agg({f'{col}': 'mean'}).reset_index()
new_col_name = f'{col}{suffix}_lag{lag}'
temp.columns = merge_cols + [new_col_name]
temp['date_block_num'] += lag
if new_col_name not in df.columns:
df = pd.merge(df, temp, on=merge_cols, how='left')
temp = None
df[new_col_name] = df[new_col_name].fillna(fill_value).astype('float32')
return (df, new_col_name)
items = pd.read_csv('../input/data-preprocessing/items.csv')
shops = pd.read_csv('../input/data-preprocessing/shops.csv')
cats = pd.read_csv('../input/data-preprocessing/item_categories.csv')
train = pd.read_csv('../input/data-preprocessing/sales_train.csv')
test = pd.read_csv('../input/data-preprocessing/test.csv').set_index('ID')
dataframes = [train, shops, items, cats]
for d in dataframes:
d = downcast(d)
import nltk
from nltk.stem import WordNetLemmatizer, SnowballStemmer
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords
stop_words = ['per', 'I', 'me', 'the', 'what', 'which', 'having', 'for', 'with', 'of', 'about', 'but', 'if', 'both', 'each', 'any', 'a']
stemmer = SnowballStemmer('english')
custom_tokenizer = RegexpTokenizer('\\w+')
def manipulate_str(a):
a = a.lower()
word_list = custom_tokenizer.tokenize(a)
stemmed_words = list()
for w in word_list:
sw = stemmer.stem(w)
if w not in stop_words and len(sw) > 2:
stemmed_words.append(sw)
return ' '.join(set(stemmed_words))
items['item_name_en_tokenized'] = items.item_name_en.apply(lambda x: manipulate_str(x))
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(min_df=15, max_features=40, stop_words='english')
vectorizer.fit(items['item_name_en_tokenized'])
text_features = vectorizer.transform(items['item_name_en_tokenized'])
text_features.shape | code |
90147004/cell_10 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import time
import numpy as np
import pandas as pd
import os
import seaborn as sns
import plotly.express as px
import time
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
mse = mean_squared_error
def downcast(df: pd.DataFrame) -> pd.DataFrame:
float_cols = [c for c in df if df[c].dtype in ['float64']]
int_cols = [c for c in df if df[c].dtype in ['int64']]
df[float_cols] = df[float_cols].astype('float32')
df[int_cols] = df[int_cols].astype('int16')
return df
def lag_feature(df: pd.DataFrame, lag: int, col: str, merge_cols, fill_value=-10, suffix=''):
temp = df[merge_cols + [col]]
temp = temp.groupby(merge_cols).agg({f'{col}': 'mean'}).reset_index()
new_col_name = f'{col}{suffix}_lag{lag}'
temp.columns = merge_cols + [new_col_name]
temp['date_block_num'] += lag
if new_col_name not in df.columns:
df = pd.merge(df, temp, on=merge_cols, how='left')
temp = None
df[new_col_name] = df[new_col_name].fillna(fill_value).astype('float32')
return (df, new_col_name)
items = pd.read_csv('../input/data-preprocessing/items.csv')
shops = pd.read_csv('../input/data-preprocessing/shops.csv')
cats = pd.read_csv('../input/data-preprocessing/item_categories.csv')
train = pd.read_csv('../input/data-preprocessing/sales_train.csv')
test = pd.read_csv('../input/data-preprocessing/test.csv').set_index('ID')
dataframes = [train, shops, items, cats]
for d in dataframes:
d = downcast(d)
tme = pd.read_csv('../input/data-preprocessing/train_monthly_extended.csv')
tme = downcast(tme)
cast_cols = ['item_cnt_month', 'days_with_sales', 'date_block_num']
tme[cast_cols] = tme[cast_cols].astype(int)
tme['item_cnt_month'] = tme['item_cnt_month'].clip(0, 50)
tme.sample(4)
cols = []
for lag in [1, 2, 3, 4, 6, 12]:
t = time.process_time()
tme, new_col = lag_feature(tme, lag, 'item_cnt_month', ['date_block_num', 'shop_id', 'item_id'], fill_value=np.nan)
elapsed_time = time.process_time() - t
cols.append(new_col)
tme, new_col = lag_feature(tme, -1, 'item_cnt_month', ['date_block_num', 'shop_id', 'item_id'], fill_value=0)
tme = tme.rename(columns={new_col: 'target'})
for lag in [1, 2, 3, 4, 12]:
t = time.process_time()
tme, new_col = lag_feature(tme, lag, 'item_cnt_month', ['date_block_num', 'shop_id'], fill_value=np.nan, suffix='s')
elapsed_time = time.process_time() - t
cols.append(new_col)
for lag in [1, 2, 3, 4, 12]:
t = time.process_time()
tme, new_col = lag_feature(tme, lag, 'item_cnt_month', ['date_block_num', 'item_id'], fill_value=np.nan, suffix='i')
elapsed_time = time.process_time() - t
cols.append(new_col)
tme['avg_item_3mo'] = ((tme['item_cnt_month_lag1'] + tme['item_cnt_month_lag2'] + tme['item_cnt_month_lag3']) / 3).astype(np.float16)
tme['diff_1yr'] = (tme['item_cnt_month_lag1'] - tme['item_cnt_month_lag12']).astype(np.float16)
tme['roc_1_2'] = tme['item_cnt_month_lag1'] / tme['item_cnt_month_lag2']
tme['roc_2_3'] = tme['item_cnt_month_lag1'] / tme['item_cnt_month_lag3']
tme['roc_1_4'] = tme['item_cnt_month_lag1'] / tme['item_cnt_month_lag4']
tme['roc_1_12'] = tme['item_cnt_month_lag1'] / tme['item_cnt_month_lag12']
tme['diff_12_34'] = tme['item_cnt_month_lag1'] + tme['item_cnt_month_lag2'] - tme['item_cnt_month_lag3'] - tme['item_cnt_month_lag4']
tme['month_num'] = (1 + tme['date_block_num'] % 12).astype(np.uint8)
tme['daydiff'] = (tme['days_no_sales_beginning'] - tme['days_with_sales']).astype(np.uint8)
print('How long does a given item stay in a shop?')
tme['item_age'] = (tme['date_block_num'] - tme.groupby('item_id')['date_block_num'].transform('min')).astype('int8')
tme['item_age'] = tme['item_age'].clip(0, 25)
tme['item_age_in_shop'] = (tme['date_block_num'] - tme.groupby(['item_id', 'shop_id'])['date_block_num'].transform('min')).astype('int8').clip(0, 25)
tme['price_greater_than80'] = (tme['item_price_avg'] > 1000).astype(int)
tme['price_var_within_month'] = (tme['item_price_max'] - tme['item_price_min']) / tme['item_price_avg']
tme['price_max_avg_within_month'] = tme['item_price_max'] - tme['item_price_avg']
for lag in [1, 2, 3]:
t = time.process_time()
print(f'Processing lag {lag} - filling strategy is for decision trees')
tme, new_col = lag_feature(tme, lag, 'price_var_within_month', ['date_block_num', 'item_id'], fill_value=np.nan, suffix='p')
elapsed_time = time.process_time() - t
print(f' -- {new_col} took {round(elapsed_time, 1)}')
cols.append(new_col)
tme.sample(4) | code |
90147004/cell_5 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import seaborn as sns
import plotly.express as px
import time
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
mse = mean_squared_error
def downcast(df: pd.DataFrame) -> pd.DataFrame:
float_cols = [c for c in df if df[c].dtype in ['float64']]
int_cols = [c for c in df if df[c].dtype in ['int64']]
df[float_cols] = df[float_cols].astype('float32')
df[int_cols] = df[int_cols].astype('int16')
return df
def lag_feature(df: pd.DataFrame, lag: int, col: str, merge_cols, fill_value=-10, suffix=''):
temp = df[merge_cols + [col]]
temp = temp.groupby(merge_cols).agg({f'{col}': 'mean'}).reset_index()
new_col_name = f'{col}{suffix}_lag{lag}'
temp.columns = merge_cols + [new_col_name]
temp['date_block_num'] += lag
if new_col_name not in df.columns:
df = pd.merge(df, temp, on=merge_cols, how='left')
temp = None
df[new_col_name] = df[new_col_name].fillna(fill_value).astype('float32')
return (df, new_col_name)
items = pd.read_csv('../input/data-preprocessing/items.csv')
shops = pd.read_csv('../input/data-preprocessing/shops.csv')
cats = pd.read_csv('../input/data-preprocessing/item_categories.csv')
train = pd.read_csv('../input/data-preprocessing/sales_train.csv')
test = pd.read_csv('../input/data-preprocessing/test.csv').set_index('ID')
dataframes = [train, shops, items, cats]
for d in dataframes:
d = downcast(d)
tme = pd.read_csv('../input/data-preprocessing/train_monthly_extended.csv')
tme = downcast(tme)
cast_cols = ['item_cnt_month', 'days_with_sales', 'date_block_num']
tme[cast_cols] = tme[cast_cols].astype(int)
tme['item_cnt_month'] = tme['item_cnt_month'].clip(0, 50)
tme.sample(4)
sns.scatterplot(data=tme.query('item_cnt_month>0 and days_with_sales>=0').sample(7000), x='days_with_sales', y='item_cnt_month', alpha=0.5) | code |
129035325/cell_21 | [
"text_html_output_1.png"
] | def outlier_removal(dataframe, features):
for feature_name in features:
Q1 = dataframe[feature_name].quantile(0.25)
Q3 = dataframe[feature_name].quantile(0.75)
IQR = Q3 - Q1
dataframe = dataframe[(dataframe[feature_name] >= Q1 - 1.5 * IQR) & (dataframe[feature_name] <= Q3 + 1.5 * IQR)]
return dataframe | code |
129035325/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
submission = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
origin = pd.read_csv('/kaggle/input/wild-blueberry-yield-prediction-dataset/WildBlueberryPollinationSimulationData.csv').drop('Row#', axis=1)
def info(train):
pass
train.describe().T | code |
129035325/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from colorama import Style, Fore
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go
from sklearn.model_selection import train_test_split, KFold
import optuna
from xgboost import XGBRegressor
from catboost import CatBoostRegressor
from lightgbm import LGBMRegressor
from sklearn.metrics import mean_absolute_error
from colorama import Style, Fore
red = Style.BRIGHT + Fore.RED
blu = Style.BRIGHT + Fore.BLUE
mgt = Style.BRIGHT + Fore.MAGENTA
grn = Style.BRIGHT + Fore.GREEN
gld = Style.BRIGHT + Fore.YELLOW
res = Style.RESET_ALL
TARGET = 'yield' | code |
129035325/cell_20 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
submission = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
origin = pd.read_csv('/kaggle/input/wild-blueberry-yield-prediction-dataset/WildBlueberryPollinationSimulationData.csv').drop('Row#', axis=1)
def info(train):
pass
train.describe().T
cont_col = [i for (i, j) in zip(test.columns, test.dtypes) if j in ["int", "float"]]
fig, axes = plt.subplots(4, 4, figsize=(30, 20))
for i, ax in enumerate(axes.flat):
sns.kdeplot(
ax=ax, data=train, x=cont_col[i], color="#F8766D", label="Train", fill=True
)
sns.kdeplot(
ax=ax, data=test, x=cont_col[i], color="#00BFC4", label="Test", fill=True
)
ax.set_title(f"{cont_col[i]} distribution")
fig.tight_layout()
plt.legend()
fig, axes = plt.subplots(4, 4, figsize=(30, 20))
for i, ax in enumerate(axes.flat):
sns.kdeplot(
ax=ax, data=train, x=cont_col[i], color="#F8766D", label="Train", fill=True
)
sns.kdeplot(
ax=ax, data=origin, x=cont_col[i], color="#00BFC4", label="Original", fill=True
)
ax.set_title(f"{cont_col[i]} distribution")
fig.tight_layout()
plt.legend()
train = train.drop(['MinOfUpperTRange', 'AverageOfUpperTRange', 'MaxOfLowerTRange', 'MinOfLowerTRange', 'AverageOfLowerTRange', 'AverageRainingDays'], axis=1)
test = test.drop(['MinOfUpperTRange', 'AverageOfUpperTRange', 'MaxOfLowerTRange', 'MinOfLowerTRange', 'AverageOfLowerTRange', 'AverageRainingDays'], axis=1)
cont_col = [i for (i, j) in zip(test.columns, test.dtypes) if j in ["int", "float"]]
fig, axes = plt.subplots(3, 3, figsize=(30, 20))
for i, ax in enumerate(axes.flat):
sns.kdeplot(
ax=ax, data=train, x=cont_col[i], color="#F8766D", label="Train", fill=True
)
sns.kdeplot(
ax=ax, data=test, x=cont_col[i], color="#00BFC4", label="Test", fill=True
)
ax.set_title(f"{cont_col[i]} distribution")
fig.tight_layout()
plt.legend()
for col in test.columns:
print(col)
if test[col].nunique() < 20:
print(test[col].value_counts())
print(test.shape) | code |
129035325/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
submission = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
origin = pd.read_csv('/kaggle/input/wild-blueberry-yield-prediction-dataset/WildBlueberryPollinationSimulationData.csv').drop('Row#', axis=1)
def info(train):
display(train.head())
print('*' * 50)
print(f'SHAPE OF THE DATA: {train.shape}')
print('*' * 50)
if ~train.isnull().sum().sum():
print('NO NULL VALUES FOUND!')
else:
print(f'NULL VALUES: {train.isnull().sum()}')
print(train.info())
info(train) | code |
129035325/cell_19 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
submission = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
origin = pd.read_csv('/kaggle/input/wild-blueberry-yield-prediction-dataset/WildBlueberryPollinationSimulationData.csv').drop('Row#', axis=1)
def info(train):
pass
train.describe().T
cont_col = [i for (i, j) in zip(test.columns, test.dtypes) if j in ["int", "float"]]
fig, axes = plt.subplots(4, 4, figsize=(30, 20))
for i, ax in enumerate(axes.flat):
sns.kdeplot(
ax=ax, data=train, x=cont_col[i], color="#F8766D", label="Train", fill=True
)
sns.kdeplot(
ax=ax, data=test, x=cont_col[i], color="#00BFC4", label="Test", fill=True
)
ax.set_title(f"{cont_col[i]} distribution")
fig.tight_layout()
plt.legend()
train = train.drop(['MinOfUpperTRange', 'AverageOfUpperTRange', 'MaxOfLowerTRange', 'MinOfLowerTRange', 'AverageOfLowerTRange', 'AverageRainingDays'], axis=1)
test = test.drop(['MinOfUpperTRange', 'AverageOfUpperTRange', 'MaxOfLowerTRange', 'MinOfLowerTRange', 'AverageOfLowerTRange', 'AverageRainingDays'], axis=1)
cat_feat = list()
for col in train.columns:
print(col)
if train[col].nunique() < 20:
cat_feat.append(col)
print(train[col].value_counts())
print(train.shape) | code |
129035325/cell_18 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
submission = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
origin = pd.read_csv('/kaggle/input/wild-blueberry-yield-prediction-dataset/WildBlueberryPollinationSimulationData.csv').drop('Row#', axis=1)
def info(train):
pass
train.describe().T
cont_col = [i for (i, j) in zip(test.columns, test.dtypes) if j in ["int", "float"]]
fig, axes = plt.subplots(4, 4, figsize=(30, 20))
for i, ax in enumerate(axes.flat):
sns.kdeplot(
ax=ax, data=train, x=cont_col[i], color="#F8766D", label="Train", fill=True
)
sns.kdeplot(
ax=ax, data=test, x=cont_col[i], color="#00BFC4", label="Test", fill=True
)
ax.set_title(f"{cont_col[i]} distribution")
fig.tight_layout()
plt.legend()
fig, axes = plt.subplots(4, 4, figsize=(30, 20))
for i, ax in enumerate(axes.flat):
sns.kdeplot(
ax=ax, data=train, x=cont_col[i], color="#F8766D", label="Train", fill=True
)
sns.kdeplot(
ax=ax, data=origin, x=cont_col[i], color="#00BFC4", label="Original", fill=True
)
ax.set_title(f"{cont_col[i]} distribution")
fig.tight_layout()
plt.legend()
train = train.drop(['MinOfUpperTRange', 'AverageOfUpperTRange', 'MaxOfLowerTRange', 'MinOfLowerTRange', 'AverageOfLowerTRange', 'AverageRainingDays'], axis=1)
test = test.drop(['MinOfUpperTRange', 'AverageOfUpperTRange', 'MaxOfLowerTRange', 'MinOfLowerTRange', 'AverageOfLowerTRange', 'AverageRainingDays'], axis=1)
cont_col = [i for i, j in zip(test.columns, test.dtypes) if j in ['int', 'float']]
fig, axes = plt.subplots(3, 3, figsize=(30, 20))
for i, ax in enumerate(axes.flat):
sns.kdeplot(ax=ax, data=train, x=cont_col[i], color='#F8766D', label='Train', fill=True)
sns.kdeplot(ax=ax, data=test, x=cont_col[i], color='#00BFC4', label='Test', fill=True)
ax.set_title(f'{cont_col[i]} distribution')
fig.tight_layout()
plt.legend() | code |
129035325/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
submission = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
origin = pd.read_csv('/kaggle/input/wild-blueberry-yield-prediction-dataset/WildBlueberryPollinationSimulationData.csv').drop('Row#', axis=1)
def info(train):
pass
train.describe().T
cont_col = [i for (i, j) in zip(test.columns, test.dtypes) if j in ["int", "float"]]
fig, axes = plt.subplots(4, 4, figsize=(30, 20))
for i, ax in enumerate(axes.flat):
sns.kdeplot(
ax=ax, data=train, x=cont_col[i], color="#F8766D", label="Train", fill=True
)
sns.kdeplot(
ax=ax, data=test, x=cont_col[i], color="#00BFC4", label="Test", fill=True
)
ax.set_title(f"{cont_col[i]} distribution")
fig.tight_layout()
plt.legend()
fig, axes = plt.subplots(4, 4, figsize=(30, 20))
for i, ax in enumerate(axes.flat):
sns.kdeplot(ax=ax, data=train, x=cont_col[i], color='#F8766D', label='Train', fill=True)
sns.kdeplot(ax=ax, data=origin, x=cont_col[i], color='#00BFC4', label='Original', fill=True)
ax.set_title(f'{cont_col[i]} distribution')
fig.tight_layout()
plt.legend() | code |
129035325/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
submission = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
origin = pd.read_csv('/kaggle/input/wild-blueberry-yield-prediction-dataset/WildBlueberryPollinationSimulationData.csv').drop('Row#', axis=1)
def info(train):
pass
train.describe().T
cont_col = [i for (i, j) in zip(test.columns, test.dtypes) if j in ["int", "float"]]
fig, axes = plt.subplots(4, 4, figsize=(30, 20))
for i, ax in enumerate(axes.flat):
sns.kdeplot(
ax=ax, data=train, x=cont_col[i], color="#F8766D", label="Train", fill=True
)
sns.kdeplot(
ax=ax, data=test, x=cont_col[i], color="#00BFC4", label="Test", fill=True
)
ax.set_title(f"{cont_col[i]} distribution")
fig.tight_layout()
plt.legend()
fig, axes = plt.subplots(4, 4, figsize=(30, 20))
for i, ax in enumerate(axes.flat):
sns.kdeplot(
ax=ax, data=train, x=cont_col[i], color="#F8766D", label="Train", fill=True
)
sns.kdeplot(
ax=ax, data=origin, x=cont_col[i], color="#00BFC4", label="Original", fill=True
)
ax.set_title(f"{cont_col[i]} distribution")
fig.tight_layout()
plt.legend()
train = train.drop(['MinOfUpperTRange', 'AverageOfUpperTRange', 'MaxOfLowerTRange', 'MinOfLowerTRange', 'AverageOfLowerTRange', 'AverageRainingDays'], axis=1)
test = test.drop(['MinOfUpperTRange', 'AverageOfUpperTRange', 'MaxOfLowerTRange', 'MinOfLowerTRange', 'AverageOfLowerTRange', 'AverageRainingDays'], axis=1)
cont_col = [i for (i, j) in zip(test.columns, test.dtypes) if j in ["int", "float"]]
fig, axes = plt.subplots(3, 3, figsize=(30, 20))
for i, ax in enumerate(axes.flat):
sns.kdeplot(
ax=ax, data=train, x=cont_col[i], color="#F8766D", label="Train", fill=True
)
sns.kdeplot(
ax=ax, data=test, x=cont_col[i], color="#00BFC4", label="Test", fill=True
)
ax.set_title(f"{cont_col[i]} distribution")
fig.tight_layout()
plt.legend()
cat_feat = list()
for col in train.columns:
if train[col].nunique() < 20:
cat_feat.append(col)
def outlier_removal(dataframe, features):
for feature_name in features:
Q1 = dataframe[feature_name].quantile(0.25)
Q3 = dataframe[feature_name].quantile(0.75)
IQR = Q3 - Q1
dataframe = dataframe[(dataframe[feature_name] >= Q1 - 1.5 * IQR) & (dataframe[feature_name] <= Q3 + 1.5 * IQR)]
return dataframe
features = train.columns
train = outlier_removal(train, features)
for col in cat_feat:
q1 = test[col].quantile(0.25)
q3 = test[col].quantile(0.75)
iqr = q3 - q1
lower_bound = q1 - 1.5 * iqr
upper_bound = q3 + 1.5 * iqr
mean_value = test[col].mean()
test.loc[(test[col] < lower_bound) | (test[col] > upper_bound), col] = mean_value | code |
129035325/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
submission = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
origin = pd.read_csv('/kaggle/input/wild-blueberry-yield-prediction-dataset/WildBlueberryPollinationSimulationData.csv').drop('Row#', axis=1)
def info(train):
pass
train.describe().T
cont_col = [i for i, j in zip(test.columns, test.dtypes) if j in ['int', 'float']]
fig, axes = plt.subplots(4, 4, figsize=(30, 20))
for i, ax in enumerate(axes.flat):
sns.kdeplot(ax=ax, data=train, x=cont_col[i], color='#F8766D', label='Train', fill=True)
sns.kdeplot(ax=ax, data=test, x=cont_col[i], color='#00BFC4', label='Test', fill=True)
ax.set_title(f'{cont_col[i]} distribution')
fig.tight_layout()
plt.legend() | code |
129035325/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
submission = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
origin = pd.read_csv('/kaggle/input/wild-blueberry-yield-prediction-dataset/WildBlueberryPollinationSimulationData.csv').drop('Row#', axis=1) | code |
105193974/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
test = pd.read_csv('../input/feedback-prize-english-language-learning/test.csv')
ss = pd.read_csv('../input/feedback-prize-english-language-learning/sample_submission.csv')
target_cols = ['cohesion', 'syntax', 'vocabulary', 'phraseology', 'grammar', 'conventions']
train[target_cols].min() | code |
105193974/cell_2 | [
"text_plain_output_1.png"
] | pwd | code |
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